The Contribution of SIASGE Radar Data Integrated With Optical Images to Support Thematic Mapping at Regional Scale

This paper aims to assess the potential of radar data combined with optical data to support local administrations in the knowledge of the land use and land cover at regional scale. The work starts from the actual available thematic maps owned by two different regional administrations in Italy to assess at what extent they can be improved or reproduced by Earth Observation data. In particular, the contribution of data available in the future through the Sistema Italo-Argentino di Satelliti per la Gestione delle Emergenze (SIASGE) project, combining L-band and X-band radar imagery, is assessed in order to produce thematic maps of the regions. Moreover, the further contribution brought by C-band and especially by optical bands has been evaluated. The classification problem is driven by the legend of already existing maps and quality checked against the same maps in order to tackle the real needs of the land managing authorities. As the combination of data from optical imagery is fundamental to achieve good thematic accuracy, the work has exploited the support vector machine (SVM) learning technique, which is more suitable than standard statistical parametric approaches in this respect. Concerning the classification steps, some algorithmic issues have been faced to improve the results, such as training set selection strategy and data fusion techniques. The work has proved that the multisource dataset (radar and optical) is fairly suitable to produce thematic maps comparable to what is already in use at local administrative level, allowing one to achieve classification accuracy in the order of 90%.